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Artificial intelligence: applications in cardiovascular medicine

Guest Editors

Michael Spartalis, MD, PhD, FESC, FEHRA, FACC, CCDS, CEPS-A, 3rd Department of Cardiology, National and Kapodistrian University of Athens, Greece
Kanhua Yin, MD, MPH, Department of Surgery, University of Missouri–Kansas City, US

BMC Cardiovascular Disorders called for submissions to our Collection on Artificial intelligence: applications in cardiovascular medicine. The progress in artificial intelligence (AI) holds promise for transforming cardiovascular medicine through its ability to facilitate novel methods for diagnosis, treatment, and prevention. Amidst this era of swift technological progress, AI stands poised to aid in early detection, risk evaluation, and prognostic assessment through the analysis of extensive datasets, ultimately resulting in enhanced patient outcomes and more effective management tactics.

New Content ItemThis Collection supports and amplifies research related to SDG 3: Good Health & Well-Being and SDG 10: Reduced Inequalities.

Meet the Guest Editors

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Michael Spartalis, MD, PhD, FESC, FEHRA, FACC, CCDS, CEPS-A, 3rd Department of Cardiology, National and Kapodistrian University of Athens, Greece

Dr Michael Spartalis is an Attending Cardiologist, Certified Electrophysiology Specialist, and Certified Cardiac Device Specialist. Currently, a Cardiac Electrophysiologist at the 3rd Department of Cardiology, National and Kapodistrian University of Athens, Clinical Scholar at Harvard Medical School, and an Expert on medical devices at the European Commission and the European Medicines Agency. His work focuses on cardiac electrophysiology, device therapy, and sudden cardiac death, and encompasses a wide array of studies related to implantable cardioverter-defibrillators, cardiac resynchronization therapy, rhythm disturbances, and technology innovations including new disruptive wearable technologies improving health care delivery. He holds an MD and a PhD from National and Kapodistrian University of Athens, Medical School, a MSc in Thrombosis, Haemorrhage and Transfusion medicine from National and Kapodistrian University of Athens, Medical School, a MHCM from Conservatoire National des Arts et Métiers, and a Master in Interventional Electrophysiology from Università Vita-Salute San Raffaele. Most recently, he completed the Oxford Leadership Program.

Kanhua Yin, MD, MPH, Department of Surgery, University of Missouri–Kansas City, US

Dr Kanhua Yin is a surgery resident at the University of Missouri-Kansas City. With a background in clinical medicine and data science, he possesses a deep-seated passion for research in cardiovascular surgery, population-based outcomes analysis, clinical informatics, and natural language processing. Dr Yin boasts an impressive portfolio with over 80 peer-reviewed publications. His first-authored papers have been included in multiple international clinical guidelines, including the 2022 ACC/AHA aortic disease guideline and the 2024 ESVS abdominal aorto-iliac artery aneurysms guideline.

About the Collection

BMC Cardiovascular Disorders called for submissions to our Collection on Artificial intelligence: applications in cardiovascular medicine. Advancements in artificial intelligence (AI) have the potential to revolutionize cardiovascular medicine by enabling innovative approaches to diagnosis, treatment, and prevention. In this era of rapid technological advancements, AI can assist in early detection, risk assessment, and prognostic evaluation by analyzing large datasets, thus leading to improved patient outcomes and better management strategies.

BMC Cardiovascular Disorders launched this Collection in alignment with the United Nations' Sustainable Development Goals (SDGs) 3: Good Health and Well-being and 10: Reduced Inequalities. The aim of this Collection is to consolidate both fundamental and clinical research to advance our comprehension of cardiovascular conditions.

BMC Cardiovascular Disorders welcomed original research on the design, implementation, optimization, and clinical impact of AI applications in the field of cardiovascular medicine. Topics of interest include, but are not limited to, the following:

• Machine learning (ML) algorithms for early detection of cardiovascular diseases
• AI applications for diagnostic accuracy studies
• AI systems as an intervention in live clinical settings
• Predictive modeling using AI for personalized risk assessment of cardiovascular disorders
• Application of AI in cardiovascular imaging analysis
• Utilizing natural language processing (NLP) and AI for analyzing electronic health records in cardiovascular care
• Application of AI and ML in cardiovascular surgery
• Wearable devices and AI algorithms for continuous monitoring of cardiovascular health
• AI-enabled precision medicine approaches for personalized treatment
• AI-powered automated risk scoring systems for cardiovascular events
• Ethical considerations and challenges in the implementation of AI in cardiovascular medicine
• AI in cardiovascular genetics
• Clinical decision support tools in cardiovascular medicine

We encouraged the use of standardized reporting guidelines for research with AI/ML components to encourage authors to provide information to allow their work to be evaluated appropriately. Reporting guidelines and checklists have been developed for a broad range of study design and research types with AI/ML components. Those that have been developed, adapted, or are planned to be adapted for research using AI/ML can be found summarized in the table below:

Reporting guideline AI guideline Study design

AI- guideline description

SPIRIT, 2013                

SPIRIT-AI, 2020            

Randomized controlled trials (protocols)

Used to report the protocols of randomized controlled trials evaluating AI systems as interventions.

CONSORT, 2010           

CONSORT-AI, 2020       

Randomized controlled trials

Used to report randomized controlled trials evaluating AI systems as interventions (large-scale, summative evaluation), independently of the AI system modality (diagnostic, prognostic, therapeutic). Focuses on effectiveness and safety.
TRIPOD, 2015           TRIPOD-AI, upcoming     

Prediction model evaluation

Used to report prediction models (diagnostic or prognostic) development, validation and updates.

STARD, 2015            

STARD-AI     

Diagnostic accuracy studies

Used to report diagnostic accuracy studies, either at development stage or as an offline validation in clinical settings.

N/A

CLAIM , 2020             

Diagnostic accuracy studies

Used to report a wide spectrum of AI applications using medical images. Contains elements of the STARD 2015 guideline. Lists information such as descriptions of ground truth, data partitions, model description, and training and evaluation steps.

N/A

DECIDE-AI, 2022             

Various (e.g. prospective cohort studies and non-randomized controlled trials) with additional features, such as modification of intervention, analysis of pre-specified subgroups or learning curve analysis.

Used to report the early evaluation of AI systems as an intervention in live clinical settings (small-scale, formative evaluation), independently of the study design and AI system modality (diagnostic, prognostic, therapeutic). Focuses on clinical utility, safety and human factors.

Image credit: Who is Danny / stock.adobe.com

  1. Heart failure (HF) after acute myocardial infarction (AMI) is a leading cause of mortality and morbidity worldwide. Accurate prediction and early identification of HF severity are crucial for initiating preven...

    Authors: Chenglong Guo, Binyu Gao, Xuexue Han, Tianxing Zhang, Tianqi Tao, Jinggang Xia and Honglei Liu
    Citation: BMC Cardiovascular Disorders 2025 25:362
    Content type: Research Published on:

  2. Heart failure (HF) impacts nearly 6 million individuals in the U.S., with a projected 46% increase by 2030, is creating significant healthcare burdens. Predictive models, particularly machine learning (ML)-bas...

    Authors: Hamed Hajishah, Danial Kazemi, Ehsan Safaee, Mohammad Javad Amini, Maral Peisepar, Mohammad Mahdi Tanhapour and Arian Tavasol
    Citation: BMC Cardiovascular Disorders 2025 25:264
    Content type: Research Published on:

  3. Congenital heart disease (CHD) represents the most common group of congenital anomalies, constitutes a significant contributor to the burden of non-communicable diseases, highlighting the critical need for imp...

    Authors: Ida Mohammadi, Shahryar Rajai Firouzabadi, Melika Hosseinpour, Mohammadhosein Akhlaghpasand, Bardia Hajikarimloo, Sam Zeraatian-Nejad and Peyman Sardari Nia
    Citation: BMC Cardiovascular Disorders 2024 24:718
    Content type: Research Published on:

    The Correction to this article has been published in BMC Cardiovascular Disorders 2025 25:14

  4. The sodium‒glucose cotransporter-2 (SGLT2) inhibitor empagliflozin (EMPA) has been demonstrated to reduce the risk of cardiovascular mortality or hospitalization for heart failure (HF) in patients. Nevertheles...

    Authors: Qingkai Yan, Xinrao Chen, Changqing Yu and Yuehui Yin
    Citation: BMC Cardiovascular Disorders 2024 24:663
    Content type: Systematic Review Published on:

  5. Late gadolinium enhancement cardiac magnetic resonance imaging (LGE-CMR) is a valuable cardiovascular imaging technique. Segmentation of cardiac chambers from LGE-CMR is a fundamental step in electrophysiologi...

    Authors: Hairui Wang, Helin Huang, Jing Wu, Nan Li, Kaihao Gu and Xiaomei Wu
    Citation: BMC Cardiovascular Disorders 2024 24:571
    Content type: Research Published on:

  6. Hypertension is a common disease, often overlooked in its early stages due to mild symptoms. And persistent elevated blood pressure can lead to adverse outcomes such as coronary heart disease, stroke, and kidn...

    Authors: Kun Guo, Weicheng Ni, Leilei Du, Yimin Zhou, Ling Cheng and Hao Zhou
    Citation: BMC Cardiovascular Disorders 2024 24:544
    Content type: Research Published on:

  7. This study aims to construct a clinical prediction model and create a visual line chart depicting the risk of acute kidney injury (AKI) following resuscitation in cardiac arrest (CA) patients. Additionally, th...

    Authors: Shanbing Hou, Lixiang Zhang, Hongzhi Ji, Tingting Zhao, Ming Hu, Ying Jiang, Quanquan Sun, Ming Zhang and Min Dou
    Citation: BMC Cardiovascular Disorders 2024 24:440
    Content type: Research Published on:

Submission Guidelines

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This Collection welcomes submission of original Research Articles. Should you wish to submit a different article type, please read our submission guidelines to confirm that type is accepted by the journal. Articles for this Collection should be submitted via our submission system, Snapp. During the submission process you will be asked whether you are submitting to a Collection, please select "Artificial intelligence: applications in cardiovascular medicine" from the dropdown menu.

Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests.